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643 results for “cattle”
Genetic characterization of Rarámuri Criollo cattle from the USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA
Rarámuri Criollo (RC) cattle have been raised by isolated Tarahumara communities of Chihuahua, Mexico, for nearly 500 years, mostly under natural selection and minimal management. The RC cattle was introduced to the USDA Jornada Experimental Range (RCJER) in 2005 to begin evaluations of beef production performance and their adaptation to the harsh ecological and climatic conditions of the Northern Chihuahuan Desert. While this research unveiled crucial information on their phenotypic plasticity and adaptation, the genetic diversity and structure of the RCJER population remain poorly understood. This study analyzed the genetic diversity, population structure, ancestral composition, and selection signatures of the RCJER herd using a ~64K SNP array. The RCJER herd exhibits moderate genetic diversity and low population stratification with no evident clustering, suggesting a shared genetic background among different subfamilies. Admixture analysis revealed the RCJER herd represents a distinctive genetic pool within the Criollo cattle biotypes, with significant Iberian ancestry. Selection signatures identified candidate genes and Quantitative Trait Loci for traits associated with milk composition, growth, meat and carcass, reproduction, metabolic homeostasis, health, and coat color. The RCJER population represents a distinctive genetic resource adapted to harsh environmental conditions while maintaining productive and reproductive attributes. These findings are crucial to ensuring the long-term genetic conservation of the RCJER and their strategic expansion to locally adapted beef production systems in the US.
Data on anatomy, movement, and foraging behaviour of three cattle breeds of different productivity
<p>Given are</p> <ul> <li>the breed of the cattle (AH: Angus×Holstein, OB: Original Braunvieh, HC: Highland cattle),</li> <li>the age of the cows in months,</li> <li>the body weight at the beginning (Weight_1) and the end (Weight_2) of the experiment in kg,</li> <li>the summarised base of all eight claws of each cow in cm<sup>2</sup>,</li> <li>the average number of steps per hour as recorded by the pedometer,</li> <li>the average speed in m h<sup>-1</sup>,</li> <li>the ratio of the time spent lying as recorded by the pedometer,</li> <li>the evenness of space use calculated as Camargo’s index based on GPS positions,</li> <li>the evenness of forage selection calculated as Pielou’s evenness,</li> <li>the average forage quality indicator value (Briemle, Nitsche, and Nitsche 2002) of the selected diet,</li> <li>the ratio of broad leaved grasses, legumes, thistles and shrubs within the diet of each cow.</li> </ul> <p>All measurements conducted on the pastures are presented as averaged over all pastures (xxx_mean) and separatly for the three pastures (xxx_1, xxx_2, xxx_3).</p>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
cGTEx_dataset:A multi-tissue atlas of regulatory variants in cattle
<p>The files are raw data of the cGTEX dataset used in the publication <strong>https://doi.org/10.1038/s41588-022-01153-5</strong>. For details, please read the Methods section. </p> <p>1. cGTEx_meta_data_8646sample.xlsx</p> <p>Metadata consists of sample names with their sample accession, including information such as data size, cleaned reads, mapping rate, and age. The data is extracted from SRA (<a href="https://www.ncbi.nlm.nih.gov/sra">https://www.ncbi.nlm.nih.gov/sra/</a>) and BIGD (<a href="https://bigd.big.ac.cn/bioproject/">https://bigd.big.ac.cn/bioproject/</a>) ( samples starting with CRS)</p> <p>2. cGTEx_count_8646sample_27607gene.txt.gz</p> <p>Data consist of raw RNA-seq read count of 27607 genes (column names as Ensembl gene id )of 8646 samples (as row names) </p> <p>3. cGTEx_TPM_8646sample_27607gene.txt.gz</p> <p>Data consist of TPM values of 27607 genes (column names as Ensembl gene id) in samples (8646 samples as row names)</p> <p>4. cGTEx_imputed_vcf.tar.gz</p> <p>Imputed genotypes (SNP) of 7297 RNA-seq samples in 29 autosomes.</p> <p>5. cGTEx_exon_junction_8646sample.tar.gz</p> <p>Exon junction files of 8646 files </p> <p>Note: Small discrepancies in some sample names or the absence of headers in some data sets compared to https://cgtex.roslin.ed.ac.uk/ are sorted out in this upload.</p> <p> </p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
Baseline survey for beef cattle producers in the Southwest and Southern Plains
This data package includes survey questions from beef cattle producers collectively operating in at least 31 counties in at least 7 states (California, Illinois, Missouri, Nebraska, New Mexico, Oklahoma, Texas) - "at least" because there were some respondents who chose not to provide the location of their operation. Responses were collected between January 22, 2020 and May 31, 2021. Most of the surveys were administered in person at the 2020 Southwest Beef Symposium in Amarillo, TX. The survey was also placed online and an additional few responses were collected through the online survey. These data represent a sample of convenience as no formal sampling scheme was employed in soliciting responses. Survey responses are summarized in the publication, Snapshot of Rancher Perspectives on Creative Cattle Management Options (Elias et. al, 2020). The purpose of gathering these data was to learn more about the characteristics of beef cattle producers in the region and to gauge producer interest in precision livestock ranching technologies and heritage cattle – both strategies being researched by the Sustainable Southwest Beef Project to support sustainability of ranching operations in the Southwest and Southern Plains regions of the US.
Jornada Experimental Range (USDA-ARS) annual stocking rates for cattle, horses, and sheep, 1916-2001
This data package contains data on stocking rates for cattle, horses, and sheep on all pastures of the USDA-ARS Jornada Experimental Range beginning in 1916. Grazing goats were infrequent and are therefore included as part of the sheep category. Stocking rates are expressed in animal unit month (AUM), which is based on metabolic weight and average amount of forage needed by each animal unit per month. Total AUM is calculated for each year for each animal unit. This study was completed in 2001 and will not be updated. NOTE: The USDA-ARS discontinued regular updates to this dataset after 2002 because of de-stocking.
PBG06 Cattle grazing and cattle performance in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on monitoring (1) the dynamics of cattle grazing on each of two sets of three pastures burned each year on a rotating basis and (2) cattle performance including cow weight gain, body condition, and reproductive performance and calf weight gains.
Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
Drone raw images of cattle in french grazing areas
<p><strong>The updated data are part of the Horizon Europe project ICAERUS</strong> regarding the livestock monitoring use case in a task where the objective is to test, optimize and scale up models regarding animal counting (cattle and sheep). More information here : <a href="https://icaerus.eu/">https://icaerus.eu/</a></p> <p>The dataset encompasses around <strong>900 raw .jpeg drone images </strong><strong>of grazing areas where cattle graze</strong> collected between June and August 2023. Drone used were Mavic 3 Enterprise and Thermal. Data collection is underway, with the aim of collecting images throughout the year and on several farms, to capture variability in animal and background colors and brightness conditions. Image tags (“cattle” vs “no cattle”) are not available for the moment but will be in the next months in next versions of this dataset. There is a strong imbalance between images with “cattle” and image with “no cattle” representative of areas to monitor. </p> <p><br> The nadir images were collected during flight planned with DJI Pilot 2 at a constant altitude regarding the take-off position (30 m, 60 m, 100 m). </p> <p>The data are organized by a first directory by farm where the images were collected and then with one directory by flight planned. <br> A summary is available in the Table_summary.xls. Name of the directory of each planned flight is defined such as DJI_YYYYMMDDHHMM_XX with the date (YYYYMMDD), the hour in UTC+2 (HHMM), and XX representing a mission number. </p> <p><br> .exif data of each images provide many information regarding the drone (GPS position, absolute and relative altitude, gimble information, speed etc.). Further details will be added in the next versions of the dataset.</p> <p><strong>The authors of the dataset are opened to any collaboration regarding animal counting models.</strong></p> <p><br> For more information, please contact: adrien.lebreton@idele.fr </p>
N cycling summary 2020-2022 of annually burned bison, cattle and ungrazed experimental watersheds on upland tallgrass prairie soils at the Konza Prairie Biological Station
Nitrogen (N) is a necessary element of soil fertility and a limiting nutrient in tallgrass prairie but grazers like bison and cattle can also recycle N. Bison and cattle impact the nitrogen (N) cycle by digesting forage that is consumed, and recycled back to the soil in a more available forms stimulating soil microbial N cycling activities. Yet we do not know how both grazers comparatively affect N cycling in tallgrass prairie. Thus, we investigated if bison cattle had similar impacts on N cycling in annually burned tallgrass prairie relative to ungrazed conditions over a 3-year period (2020-2022) at the Konza Prairie Biological Station. We took soil samples to investigate soil data: pH, soil water content, mineralized N, nitrification potential, denitrification potential and extracellular enzyme assays on upland soils of the Florence-Benfield complex soil map during the summer growing season from 2020 to 2022 on bison, cattle and ungrazed experimental watersheds at the Konza Prairie Biological Station. Soil sampling was undertaken once late in each summer growing season from 2020-2022. These years spanned a range of above-average rainfall (2020) to well below average (2021) and slightly below average (2022). We sampled along four 10-m transects, parallel to long-term plant sampling transects in each experimental watershed, in two bison grazed (N1A and N1B), two cattle grazed (C1A and C1B), and two ungrazed (1D and SpB) watersheds, all of which are burned annually.
Identifying hotspots of soil legacy phosphorus for soil P remediation on a cattle ranch in the Headwaters of the Everglades, South Central Florida, USA, 2020.
Phosphorus (P) cycling has been altered by human activities across various scales. 'Soil legacy P,' driven by agricultural changes such as excessive P fertilization and manure input, has led to P accumulation in soils. These legacy P reserves are long-term non-point sources, causing downstream eutrophication. Despite considerable scientific and policy interest, the fine-scale spatial heterogeneity, underlying drivers, and scales of variance of legacy P remain poorly understood. This dataset comprises of 1,438 surface soils sampled in 2020 across two typical subtropical grasslands managed for livestock production in South Central Florida, USA. The types of grasslands sampled were Intensively-managed or Improved pastures (IM), and Semi-native (SN) pastures. Chemical analysis was performed on the soil samples to determine three soil legacy P measurements (total P, Mehlich-1 and Mehlich-3 extractable P representing labile P pools) across the landscape. Other variables analyzed includedsoil organic matter, pH, available Fe and Al. Additionally, aboveground biomass samples were collected at a subset of soil sites, and analyzed for P content. The key questions regarding soil legacy P related to: its spatial variability and hotspots, variance distribution, relationship to land management and soil characteristics, and correlation with aboveground plant tissue P concentration. Subsequent analysis and spatial autoregressive modeling from this dataset revealed extreme variability of soil P at small scales, with diminishing variance as spatial scale increased, and increased variance in IM vs SN pastures. These findings enhance our understanding of the underlying drivers, spatial patterns, and variances of soil legacy P. Research suggests that broad pasture- or farm-level best management practices may be limited and less efficient, particularly for high-intensity pastures. Instead, management strategies to reduce soil legacy P could be implemented at fine scales, targeting P hots
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Dataset for: Cattle aggregations at shared resources create potential parasite exposure hotspots for wildlife
<p>Globally rising livestock populations and declining wildlife numbers are likely to dramatically change disease risk for wildlife and livestock, especially at resources where they congregate. However, limited understanding of interspecific transmission dynamics at these hotspots hinders disease prediction or mitigation. In this study, we combined gastrointestinal nematode density and host foraging activity measurements from our prior work in this system with three estimates of parasite-sharing capacity to investigate how interspecific exposures alter the relative riskiness of an important resource – water – among cattle and five dominant herbivore species in an East African tropical savanna. </p> <p>We found that due to their high parasite output, water dependence, and parasite-sharing capacity, cattle greatly increased potential parasite exposures at water sources for wild ruminants. When untreated for parasites, cattle accounted for over two-thirds of total potential exposures around water for wild ruminants, driving 2–23-fold increases in relative exposure levels at water sources. Simulated changes in wildlife and cattle ratios showed that water sources become increasingly important hotspots of interspecific transmission for wild ruminants when the relative abundance of cattle parasites increases. These results emphasize that livestock have significant potential to alter the level and distribution of parasite exposures across the landscape for wild ruminants.</p>
Smartcow EU project database on feed efficiency in beef cattle and analysis of natural 15N abundance and plasma urea concentration
<p>This is a database built in the frame of the Smartcow H2020 Eu project (N°730924) and gathering individual raw data for animal performances obtained in thirteen beef cattle trials conducted in France, UK and Switzerland as well as animal values for two biomakers of feed efficiency : the natural 15N abundance in animal proteins (plasma or muscle) and plasma urea concentration. </p>
Data from: Long-term cattle grazing shifts the ecological state of forest soils
<p><span>Cattle grazing profoundly affects abiotic and biotic characteristics of ecosystems. While most research has been performed on grasslands, the effect of large managed ungulates on forest ecosystems has largely been neglected.</span></p> <p><span>Compared to a baseline semi-natural state, we investigated how long-term cattle grazing of birch forest patches affected the abiotic state and the ecological community (microbes and invertebrates) of the soil subsystem.</span></p> <p><span>Grazing strongly modified the soil abiotic environment by increasing phosphorus content, pH and bulk density, while reducing the C:N ratio. The reduced C:N-ratio was strongly associated with a lower microbial biomass, mainly caused by a reduction of fungal biomass. This was linked to a decrease in fungivorous nematode abundance and the nematode channel index, indicating </span><span>a relative </span><span>uplift in the importance of the bacterial energy-channel in the nematode assemblages. </span></p> <p><span>Cattle grazing highly modified invertebrate community composition producing distinct assemblages from the semi-natural situation. Richness and abundance of microarthropods was consistently reduced by grazing (excepting collembolan richness) and grazing-associated changes in soil pH, Olsen P and reduced soil pore volume (bulk density) limiting niche space and refuge from physical disturbance. Anecic earthworm species predominated in grazed patches, but were absent from ungrazed forest, and may benefit from manure inputs, while their deep vertical burrowing behaviour protects them from physical disturbance.</span></p> <p><span>Perturbation of birch forest habitat by long-term ungulate grazing profoundly modified soil biodiversity, either </span><span>directly through increased physical disturbance and manure input or indirectly by modifying soil abiotic conditions.</span><span> Comparative analyses revealed the ecosystem engineering potential of large ungulate grazers in forest systems through major shifts in the composition and structure of microbial and invertebrate assemblages, including the potential for reduced energy flow through the fungal decomposition pathway. The precise consequences for species trophic interactions and biodiversity-ecosystem function relationships remains to be established, however. </span></p>
Figure 2 in Infestation of Zebu cattle (Bos indicus Linnaeus) by hard ticks (Acari: Ixodidae) in Maiduguri, Northeastern Nigeria
Figure 2. Numbers of individual ticks of different species collected from different body parts of cattle.
Fig. 1 in Identification and characterization of Rhipicephalus (Boophilus) microplus candidate protective antigens for the control of cattle tick infestations
Fig. 1 Antibody response in vaccinated cattle. Bovine serum antibody titers to recombinant antigens were determined by ELISA in cattle vaccinated with ubiquitin, subolesin, Bm86, and adjuvant/ saline control. Antibody titers in immunized cattle were expressed as the OD450 nm value for the highest serum dilution (1:1,000) and compared between vaccinated and control cattle using an ANOVA test (*P<0.05). The time of vaccination shots (arrows) and tick infestation are indicated
Fig. 6 Monthly anti-F in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 6 Monthly anti-F. hepatica antibody levels in bulk tank milk (BTM) (solid line) and average serum antibody levels of milking cows during the study period (triangle points with dashed line, error bars showing standard error of the mean) in the four farms
Fig. 5 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
Fig. 5 The summary of F. hepatica diagnostic test results according to farms and age during the study period (from spring 2015 to winter 2017). Colour indicates animals that were born in the same year. Coproantigen ELISA values are log-transformed (after adding a fixed constant of 1), and the cut-off defined as 1.89 (1.061 after transformation). Faecal egg counts in 5 g faeces were also log-transformed (after adding a fixed constant of 1) for the benefit of visualisation. Any post-treatment data are excluded
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